Method for locating an impact on a surface and device for implementing such a method

ABSTRACT

Method for locating an impact on a surface ( 9 ), in which acoustic sensors ( 6 ) pick up acoustic signals s ki  (t) generated by the impact and the impact is located by calculating, for a number of reference points of index j, a validation parameter representative of a function: PROD kj   i   1   i   2   . . . i   2p  (ω)=øS ki1  (ω) ØR ji1 (ω)*øS ki2  (ω)*øR ji2  (ω) . . . ØS ki2p  (ω)*ØR ji2p  (ω) where: ØS ki  (ω) and ØR ji  (ω)* are complex phases of S ki  (ω) and of R ji  (ω), for i=i 1 , i 2 , . . . , i 2p , indices denoting sensors, S ki (ω) and R ji (ω) being the Fourier transform of s ki  (t) and r ji  (t), r ji  (t) being a reference signal corresponding to the sensor i for an impact at the reference point j, p being a non-zero integer no greater than N SENS /2.

The present invention relates to the methods for locating an impact on a surface and to the devices for implementing such methods.

More particularly, the invention relates to a method in which an impact is located on a surface belonging to an object provided with at least N_(SENS) acoustic sensors (the object forming the acoustic interface can be made of a single piece or of several items, joined together or at least in mutual contact), N_(SENS) being a natural integer at least equal to 2,

method in which:

-   -   N_(SENS) signals s_(ki)(t) from acoustic waves generated in the         object by said impact are respectively sensed by the sensors, i         being an index between 1 and N_(SENS) which denotes the         corresponding sensor,     -   for each reference point of index j, at least one validation         parameter representative of at least one intercorrelation of         complex phases representative of said signals s_(ki)(t) and of         reference signals r_(ji)(t) is calculated, each reference signal         r_(ji)(t) corresponding to the signal that would be received by         the sensor i in case of impact at a reference point j out of         N_(REF) reference points belonging to said surface, N_(REF)         being a natural integer at least equal to 1 and j being an index         between 1 and N_(REF),     -   and the impact is located by determining at least one reference         point as close as possible to the point of impact, by applying         at least one validation criterion to the validation parameter.

Document WO-A-03/107261 describes an example of such a method which already gives total satisfaction. The main object of the present invention is to further refine this method, in particular to obtain an even more stable and more reliable signal processing.

To this end, according to the invention, a method of the type concerned is characterized in that said validation parameter is representative of at least one intercorrelation:

PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)−φS _(ki1)(ω)φR _(ji1)(ω)*φS _(ki2)(ω)*φR _(ji2)(ω) . . . φS_(ki2p)(ω)*φR _(ji2p)(ω)

where:

-   -   φS_(ki)(W) is the complex phase of S_(ki)(ω), for i=i₁, i₂, . .         . , i_(2p),     -   φR_(ji)(ω) is the complex phase of R_(ji)(ω), for i=i₁, i₂, . .         . , , i_(2p),     -   * denotes the conjugate operator, applied to φS_(ki)(ω) for         i=i_(2m) and to φR_(ji)(ω) for i=i_(2m-1), m being an integer         between 1 and p;     -   S_(ki)(U) is the Fourier transform of s_(ki)(t),     -   R_(ji)(ω) is the Fourier transform of r_(ji)(t),     -   p is a non-zero natural integer less than or equal to         N_(SENS)/2,     -   i₁, i₂, . . . i_(2p) are 2p indices denoting 2p sensors, each         between 1 and N_(SENS).

With these provisions, an impact positioning method is obtained which is particularly reliable, in particular because the abovementioned intercorrelation is independent of the type of impact and of the response from the sensors.

In various embodiments of the invention, there may, if necessary, be used also one and/or other of the following provisions:

-   -   the method includes a step for calculating the intercorrelation         PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω), then a step for the         inverse Fourier transform of this intercorrelation to obtain a         time function PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t) from         which said validation parameter is then calculated;     -   the intercorrelation PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)         is standardized before proceeding with the inverse Fourier         transform;     -   for each reference point j, a resemblance function V_(kj)(t) is         calculated, chosen from:

prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t),

-   -   and a linear combination of a number of functions prod_(kj) ^(i)         ₁ ^(i) ₂ ^(. . . i) _(2p)(t) corresponding to a number of         subsets of 2p sensors out of N_(SENS);     -   N_(SENS) is an even number and V_(kj)(t) is proportional to         prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t), where p=N_(SENS)/2;     -   N_(SENS)=3 and, for each reference point j, a resemblance         function V_(kj)(t) is calculated, chosen from:

V _(kj)(t)=a3.[prod _(kj) ¹²(t)+prod _(kj) ²³(t)],

and V _(kj)(t)b3. [prod _(kj) ¹²(t)+prod _(kj) ²³(t)+prod _(kj) ¹³(t)]

a3 and b3 being constants;

-   -   a3=½ and b3=⅓;     -   N_(SENS)=4 and, for each reference point j, a resemblance         function V_(kj)(t) is calculated, chosen from:

V _(kj)(t)=a4.prod _(kj) ¹²³⁴(t),

V _(kj)(t)=b4.[prod _(kj) ¹²(t)+prod _(kj) ³⁴(t)],

V _(kj)(t)=c4. [prod _(kj) ¹²(t)+prod _(kj) ²³(t)+prod _(kj) ³⁴(t)+prod _(kj) ¹⁴(t)]

a4, b4 and c4 being constants;

-   -   a4=1, b4=½ and c4=¼;     -   the validation parameter is chosen from:

$\mspace{79mu} {{{MAXIMUM}_{0j} = {{V_{kj}\left( {t = 0} \right)}}},\mspace{79mu} {{MAXIMUM}_{1j} = {{Max}\left( {{V_{kj}(t)}} \right)}},{{CONTRAST}_{1j} = \frac{{Max}\left( {MAXIMUM}_{0j} \right)}{\left( {{\Sigma_{j}{MAXIMUM}_{1j}} - {{Max}\left( {MAXIMUM}_{1j} \right)}} \right)/\left( {N_{REF} - 1} \right)}}}$      ENERGY = Σ_(i)(Σ_(t)[S_(ki)(t)]²);

-   -   only V_(kj)(t) is calculated for the time t=0 roughly         corresponding to the impact, as being the real part of PROD_(kj)         ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω), and MAXIMUM_(0j) is used as         validation parameter;     -   at least one validation criterion is used, chosen from the         following criteria:         -   CONTRAST_(1j)>THRESHOLD₁, with THRESHOLD₁>1,         -   CONTRAST_(1j)>THRESHOLD₁ and         -   MAXIMUM_(0j)/MAXIMUM_(1j) ^(NOISE)>THRESHOLD₂, where             THRESHOLD₂>1 and MAXIMUM_(1j) ^(NOISE) corresponds to the             parameter MAXIMUM_(1j) of the signals processed previously             and not having resulted in validation,         -   MAXIMUM_(0j)>THRESHOLD₃, with THRESHOLD₃>0 and             MAXIMUM_(0j)/MAXIMUM_(0j) ^(NOISE)>THRESHOLD₄ with             THRESHOLD₄>1, where MAXIMUM_(0j) ^(NOISE) corresponds to the             parameter MAXIMUM_(0j) of the signals processed previously             and not having resulted in validation,         -   MAXIMUM_(0j)/Average(MAXIMUM_(0j) ^(NOISE))>THRESHOLD₅, with             THRESHOLD₅>1,         -   ENERGY/ENERGY^(NOISE)>THRESHOLD₆, where THRESHOLD₆>1 and             ENERGY^(NOISE) corresponds to the parameter ENERGY of the             signals processed previously and not having resulted in             validation;     -   the reference signals are predetermined theoretically;     -   the reference signals are used with said object without training         phase;     -   the reference signals are previously learned on a reference         device identical to said object, then are used with said object         without training phase.

Moreover, the invention also relates to a device specially adapted to implement a method as defined above.

Other characteristics and advantages of the invention will become apparent from the following description of one of its embodiments, given by way of non-limiting example, in light of the appended drawings.

In the drawings:

FIG. 1 is a perspective diagrammatic view showing an exemplary device comprising an acoustic interface designed to implement a method according to an embodiment of the invention,

and FIG. 2 is a block diagram of the device of FIG. 1.

In the different figures, the same references denote identical or similar elements.

FIG. 1 represents a device 1 designed to implement the present invention, which includes, for example:

-   -   a microcomputer central processing unit 2,     -   a screen 3 linked to the central processing unit 2,     -   and a solid object 5 on which the central processing unit 2 can         identify the position of an impact, as will be explained below.

The object 5 can be of any type (table, shelf, window pane, wall, door, window, computer screen, display panel, interactive terminal, toy, vehicle dashboard, seat back, floor, vehicle shock absorber, etc.) in which acoustic waves (in particular Lamb waves) can be made to propagate by generating impacts on its surface 9, as will be explained below.

At least two acoustic sensors 6 are fixed to the object 5 and are linked, for example, to microphone inputs 7 of the central processing unit 2, via cables 8 or by any other transmission means (radio, infrared or other), so that said acoustic waves can be captured and transmitted to the central processing unit 2. The term N_(SENS) will hereinafter be used to denote the number of sensors and each sensor will be identified by an index from 1 to N_(SENS).

The acoustic sensors 6 can be, for example, piezo-electric sensors, or others (for example, capacitive sensors, magnetostrictive sensors, electromagnetic sensors, acoustic velocimeters, optical sensors [laser interferometers, laser vibrometers, etc.], etc.). They can be designed to measure, for example, the amplitudes of the movements due to the propagation of the acoustic waves in the object 5, or even the speed or the acceleration of such movements, or there may even be a pressure sensor measuring the pressure variations due to the propagation of the acoustic waves in the object 5.

To enable the central processing unit 2 to locate an impact on the surface 9, the signals received by the sensors i when an impact is generated at a certain number N_(REF) of reference points 10 (each identified by an index j from 1 to N_(REF)) on the surface 9 are first of all determined. In the example represented in FIG. 1, the surface 9 forms an acoustic interface in keyboard form, and the areas forming the reference points 10 can, if necessary, be marked by markings delimiting these areas and the information associated with them.

To this end, the first stage of the method can be a training step during which impacts are generated at reference points j of the surface 9.

These impacts can be generated, for example, by successively exciting the reference points j with any tool (including a part of the human body, such as a nail), advantageously a tool, the contact surface of which remains constant in time. The force of the impact is, for example, perpendicular to the surface 9 or oriented in a constant direction.

On each impact, the impulse responses are detected by the sensors 6 and stored by the central processing unit 2 to form a bank of so-called reference signals, denoted r_(ji)(t) (reference signal detected by the sensor numbered i for an excitation of the reference point numbered j). There are N_(SENS)*N_(REF) of these reference signals.

As a variant, the reference signals are predetermined theoretically, and, where appropriate, then used with said object 5 without training phase.

According to another variant, the reference signals can be learned previously on a reference device identical to said object 5, then, where appropriate, are used with said object 5 without learning phase.

The reference signals r_(ji)(t) can be expressed as follows:

r _(ji)(t)=e _(j)(t)*h _(ji)(t)*m _(i)(t)  (1)

With:

-   e_(j)(t) time function of the excitation force, -   h_(ji)(t) impulse response (Green's function) for a force applied to     the point j and detected by the sensor i, -   m_(i)(t) impulse response of the sensor i, -   * symbol representing the time convolution operator.

By switching to the frequency domain, the equation (1) becomes:

R _(ji)(ω)=E _(j)(ω)H _(ji)(ω)M _(i)(ω)  (2)

With:

E_(j)(ω): Fourier transform of e_(j)(t) H_(ji)(ω): Fourier transform of h_(ji)(t) M_(i)(ω): Fourier transform of m_(i)(t)

After the training step, the device 1 is used to locate any impact at a point numbered k of the surface 9. The N_(SENS) signals newly detected by the sensors i, denoted s_(ki)(t) are then compared with the reference signals r_(ji)(t) stored previously, so as to determine whether the point of impact k corresponds to a known reference point j.

To this end, S_(ki)(ω), the N_(SENS) Fourier transforms of the signals s_(ki)(t) are determined first of all, then the products M_(kji) of the exponential phases of the new signals S_(ki)(ω) with the phases of the signals R_(ji)(ω)* are then determined (the index i corresponds to the number of the sensor and the sign indicates the conjugate complex). To simplify the notation, the prefix φ will be used below to indicate the exponential phase of a complex variable, for example: φS_(ki)(ω) is the exponential phase of S_(ki)(ω) and φR_(ji)(ω) is the exponential phase of R_(ji)(ω).

Each product M_(kji) can therefore be written:

M _(kji)(ω)=φS _(ki)(ω)φR _(ji)(ω)*  (3)

If S_(ki)(ω) and R_(ji)(ω) are broken down according to the equation (2):

S _(ki)(ω)=E _(k)(ω)H _(ki)(ω)M ₁(ω)

and R _(ji)(ω)=E _(j)(ω)H _(ji)(ω)M ₁(ω)

Hence:

M _(kji)(ω)=φE _(k)(ω)φH _(ki)(ω)φM _(i)(ω)φE _(j)(ω)*φH _(ji)(ω)*φM _(i)(ω)*  (3′)

One or more correlation products PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω) are then calculated, respectively by correlation of an even number 2p of signals M_(kji)(ω) originating from 2p sensors i₁, i₂, . . . i_(2p) out of the N_(SENS) sensors (p is an integer from 1 to N_(SENS)/2)

PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)=M _(kji1)(ω)M _(kji2)(ω)* . . . M _(kji2p)(ω)*  (4)

It will be noted that the conjugation operator * is applied to M_(kji)(CO)ωS_(ki)(CO) with i=i_(2m), m being an integer between 1 and p.

If the formula (4) is developed, given that:

φE _(k)(ω)φE _(k)(ω)*φE _(j)(ω)φ_(j)(ω)*=1

and φM ₁(ω)φM ₁(ω)*φM ₂(ω)φM ₂(ω)=1,

we obtain:

PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)=φH _(ji1)(ω)*φH _(ki1)(ω)φH _(ji2)(ω)*φH _(ki2)(ω) . . . φH _(j2)(ω)*φH _(k2p)(ω)  (4′)

It can be noted that PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω) does not depend on the type of excitation or on the response of the sensors, which makes it an extremely interesting variable in comparing the signal received from the impact at the point k with the reference signals of the signal bank, in order to determine the position of the point k.

The PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω) value or values can be standardized, for example as follows:

PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p) ^(N)(ω)=(N _(pts) /I ^(kji) ₁ ^(i) ₂ ^(. . . i) _(2p))*PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)  (5)

Where:

PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p) ^(N)(ω) is the standardized value of PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω), N_(pts)=is the duration of the time signals s_(ki)(t) and r_(ji)(t) picked up by the sensors 6 and stored by the central processing unit 2, that is, the number of points of each of these signals after sampling and digitization,

I ^(kji) ₁ ^(i) ₂ ^(. . . i) _(2p)==Σ_(ω) |PROD _(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)  (6)

(I^(kji) ₁ ^(i) ₂ ^(. . . i) _(2p) is the integral of |PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω)| over the frequencies).

It will be noted that the standardized value can, if necessary, be calculated directly, without separately determining PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω).

The next step is to return to the time domain by calculating the inverse Fourier transform of PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω), or prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t).

To determine whether the point k corresponds to one of the reference points j₀, resemblance functions V_(kj)(t) are used, which can, depending on case, be equal (or more generally proportional) respectively to:

-   -   prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t),     -   or a linear combination (in particular an average) of a number         of functions prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t)         corresponding to a number of subsets of 2p sensors.

As an example, for N_(SENS)=2 sensors, the resemblance functions V_(ki)(t) are equal (or more generally proportional) respectively to prod_(kj) ¹²(t) (i₁=1, i₂=2 and p=1).

For N_(SENS)=3 sensors, the resemblance functions V_(ki)(t) can be chosen to be equal (or more generally proportional) respectively to:

-   -   either ½[prod_(kj) ¹²(t)+prod_(kj) ²³(t)] (respectively i₁=1,         i₂=2 and p=1 for prod_(kj) ¹²(t), i₁=2, i₂=3 and p=1 for         prod_(kj) ²³ (t)),     -   or ⅓·[prod_(kj) ¹²(t)+prod_(kj) ²³(t)+prod_(kj) ¹³(t)]         (respectively i₁=1, i₂=2 and p=1 for prod_(kj) ¹²(t), i₁=2, i₂=3         and p=1 for prod_(kj) ²³(t), i₁=1, i₂=3 and p=1 for prod_(kj) ¹³         (t))

For N_(SENS)=4 sensors, the resemblance functions V_(ki)(t) can be chosen to be equal (or more generally proportional) respectively to:

-   -   prod_(kj) ¹²³⁴(t) (i₁=1, i₂=2, i₃=3, i₄=4 and p=2),     -   or ½·[prod_(kj) ¹²(t)+prod_(kj) ³⁴(t)] (respectively i₁=1, i₂=2         and p=1 for prod_(kj) ¹²(t), i₁=3, i₂=4 and p=1 for prod_(kj) ³⁴         (t))     -   or ¼. [prod_(kj) ¹²(t)+prod_(kj) ²³ (t)+prod_(kj)         ³⁴(t)+prod_(kj) ¹⁴(t)] (respectively i₁=1, i₂=2 and p=1 for         prod_(kj) ¹²(t), i₁=2, i₂=3 and p=1 for prod_(kj) ²³(t), i₁=3,         i₂=4 and p=1 for prod_(kj) ³⁴(t), i₁=1, i₂=4 and p=1 for         prod_(kj) ¹⁴ (t)).

For a number of sensors greater than 4, the procedure is similar to the cases described above, as explained previously, by calculating the functions V_(kj)(t) as being equal (or more generally proportional) respectively to:

-   -   prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t) (in particular if         N_(SENS)=2p, and i₁=1, i₂₌₂, i₃=3, . . . , i_(2p)=N_(SENS)),     -   or a linear combination of a number of functions prod_(kj) ^(i)         ₁ ^(i) ₂ ^(. . . i) _(2p)(t) corresponding to a number of         subsets of 2p sensors out of N_(SENS): in particular, V_(kj)(t)         can be equal (or more generally proportional) to 1/n times the         sum of n functions prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t)         corresponding to n different subgroups of 2p sensors out of         N_(SENS), each sensor i preferably being included at least once         in these subgroups.

In these different examples, each resemblance function V_(kj)(t) is between −1 and 1.

With the resemblance functions determined, these functions are used to calculate one or more validation parameters.

As an example, one or more of the following validation parameters can be calculated:

     MAXIMUM_(0j) = V_(kj)(t = 0),      MAXIMUM_(1j) = Max(V_(kj)(t)) ${CONTRAST}_{1j} = \frac{{Max}\left( {MAXIMUM}_{0j} \right)}{\left( {{\Sigma_{j}{MAXIMUM}_{1j}} - {{Max}\left( {MAXIMUM}_{1j} \right)}} \right)/\left( {N_{REF} - 1} \right)}$      ENERGY = Σ_(i)(Σ_(t)[S_(ki)(t)]²).

Thus, it is possible to validate an impact located at the point k and confirm that it is located at a reference point j0, for example, if it satisfies at least one of the following groups of criteria:

1) Group 1:

The impact k is considered located at the point j0 if: CONTRAST_(1j0)>CONTRAST_(1j) for j different from j₀ and CONTRAST_(1j0)>THRESHOLD₁, with THRESHOLD₁>1, for example THRESHOLD₁=2.

2) Group 2:

The impact k is considered located at the point j0 if CONTRAST_(1j0)>CONTRAST_(1j) for j different from j₀ and

CONTRAST_(1j0)>THRESHOLD₁ and

MAXIMUM_(0j0)/MAXIMUM_(1j0) ^(NOISE)>THRESHOLD₂

where THRESHOLD₂>1, for example THRESHOLD₂=2, and MAXIMUM_(1j0) ^(NOISE) corresponds to the parameter MAXIMUM_(1j0) (averaged or not) of the signals processed previously and not having resulted in validation. This criterion avoids validating noise or random successive impacts on the object 5.

3) Group 3:

The impact k is considered located at the point j0 if MAXIMUM_(0j0)>MAXIMUM_(0j) for j different from j₀ and

MAXIMUM_(0j0)>THRESHOLD₃, with THRESHOLD₃>0, for example THRESHOLD₃ 0.5,

and

MAXIMUM_(0j0)/MAXIMUM_(0j0) ^(NOISE)>THRESHOLD₄ with THRESHOLD₄>1, for example THRESHOLD₄=2.

MAXIMUM_(0j0) ^(NOISE) corresponds to the parameter MAXIMUM_(0j0) (averaged or not) of the signals processed previously and not having resulted in validation.

Furthermore, for refinement purposes, the following criterion can be added:

MAXIMUM_(0j0)/Average(MAXIMUM_(0j0) ^(NOISE))>THRESHOLD,  (7)

with THRESHOLD₅>1, for example THRESHOLD₅=4

In addition to these criteria, the following energy criterion can also be added:

ENERGY/ENERGY^(NOISE)>THRESHOLD,  (8)

where THRESHOLD₆>1, for example THRESHOLD₆=2 and ENERGY^(NOISE) corresponds to the parameter ENERGY (averaged or not) of the signals processed previously and not having resulted in validation.

As an example, it is therefore possible to validate a point of impact k and determine that it corresponds to a reference point j0 by using one of the following combinations of criteria and groups of criteria:

-   -   (group 1),     -   (group 2),     -   (group 3),     -   (group 1) or (group 2),     -   (group 2) or (group 3),     -   (group 1) or (group 3),     -   (group 1) or (group 2) or (group 3),     -   (group 1) and (7),     -   (group 2) and (7),     -   (group 3) and (7),     -   [(group 1) or (group 2)] and (7),     -   [(group 2) or (group 3)] and (7),     -   [(group 1) or (group 3)] and (7),     -   [(group 1) or (group 2) or (group 3)] and (7),     -   (group 1) and (8),     -   (group 2) and (8),     -   (group 3) and (8),     -   [(group 1) or (group 2)] and (8),     -   [(group 2) or (group 3)] and (8),     -   [(group 1) or (group 3)] and (8),     -   [(group 1) or (group 2) or (group 3)] and (8),     -   (group 1) and (7) and (8),     -   (group 2) and (7) and (8),     -   (group 3) and (7) and (8),     -   [(group 1) or (group 2)] and (7) and (8),     -   [(group 2) or (group 3)] and (7) and (8),     -   [(group 1) or (group 3)] and (7) and (8),     -   [(group 1) or (group 2) or (group 3)] and (7) and (8).

The central processing unit 2 can thus locate the point of impact k on the surface 9 of the object 5. The determination of this point of impact may, if necessary, be the only information sought by the central processing unit, or it may even, if necessary, be used by said central processing unit 2 to deduce other information from it, for example a predetermined item of information assigned to a location on the surface 9 (the surface 9 can thus constitute an acoustic keypad). Said information assigned to a location on the surface 9 can be predetermined information assigned in advance to said location, or even information determined dynamically on each new impact on the surface 9, according to impacts previously detected.

It will be noted that, when the parameter MAXIMUM_(0j) is used to validate the impacts, for example when the (group 3) is used as the validation criterion, it is possible to calculate values approximating to MAXIMUM_(0j) very simply and quickly. In practice, when k is equal to j, terms of type φH_(j1)(ω)*φH_(k1)(ω) of the equation (4′) are purely real. Now, simply summing the real part of the product PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω) amounts to calculating the inverse Fourier transform of the equation (4′) at the time t=0.

According to this variant, the real part of each product PROD_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(ω) is therefore calculated, which gives a value approximating to the value of prod_(kj) ^(i) ₁ ^(i) ₂ ^(. . . i) _(2p)(t) at t=0. From this, the value of V_(kj)(0) is then deduced as explained previously, which gives the value of the parameter MAXIMUM_(0j), then the or each required validation criterion, for example the abovementioned group 3, is applied.

With this variant, the calculation load is far less and continuous monitoring is much more easily possible, even with a large number of reference points.

Moreover, the point of impact k on the surface 9 can be positioned, even when it is not on one of the reference points, by interpolation, as explained in the abovementioned document WO-A-03/107261.

Moreover, the reference signals can theoretically be modelled and applied to real objects, the acoustic characteristics of which are the same as those of the object concerned and used in the modelling.

The reference signals learned or theoretically modelled can be applied without training phase to objects having acoustic characteristics identical to those of the object used for training the reference signals or considered and used in the modelling. 

1. Method for locating an impact on a surface belonging to an object provided with at least N_(SENS) acoustic sensors, N_(SENS) being a natural integer at least equal to 2, in which method: the sensors (6) are made to pick up respectively N_(SENS) signals S_(ki)(t) from acoustic waves generated in the object by said impact, i being an index between 1 and N_(SENS) which denotes the corresponding sensor, where k is the area where the impact occurred there is calculated, for each reference point of index j, at least one validation parameter representative of at least one intercorrelation of complex phrases representative of said picked up signals S_(ki)(t) and of reference signals r_(ji)(t, each reference signal r_(ji)(t) corresponding to the signal that would be received by the sensor i in case of impact at a reference point j out of N_(REP) reference points belonging to said surface, N_(REF) being a natural integer at least equal to 1 and j being an index between 1 and N_(REF), and the impact is located by determining at least one reference point as close as possible to the point of impact, by applying at least one validation criterion to the validation parameter, characterized in that said validation parameter is representative of at least one intercorrelation PROD _(kj) ^(i) ₁ ^(i) ₂ ^(j) _(2p) ^((ω)) =øS _(ki1)(ω)φR _(ji1)(ω)*øS _(ki2(ω)*ø) R _(ji2)(ω) . . . øS _(ki2p)(ω)*øR _(ji2p)(ω) øS_(ki)(ω) is the complex phase of S_(ki)(ω), for i=i₁, i₂, . . . , i_(2p) øR_(ji)(ω) is the complex phase of R_(ji)(ω), for i=i₁, i₂, . . . i_(2p) * denotes the conjugate operator, applied to øS_(ki)(ω) for i=i_(2m) and to øR_(ji)(ω) for i=i_(2m-1), m is an integer between 1 and p; S_(ki)(ω) is the Fourier transform of S_(k)(t), R_(ji)(ω) is the Fourier transform of r_(ji)(t), p is a non-zero natural integer less than or equal to N_(SENS)/2, i₁, i₂, . . . i_(2p) are 2p indices denoting 2p sensors, each between 1 and N_(SENS).
 2. Method according to claim 1, including a step for calculating the intercorrelation PROD_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((ω)), then a step for the inverse Fourier transform of this intercorrelation to obtain a time function prod_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((i)), from which said validation parameter is then calculated.
 3. Method according to claim 2, in which the intercorrelation PROD_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((ω)) is standardized before proceeding with the inverse Fourier transform.
 4. Method according to claim 2, in which, for each reference point j, a resemblance function V_(kj)(t) is calculated, chosen from: prod_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p)(t), and a linear combination of a number of functions prod_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((t)) corresponding to a number of subsets of 2p sensors out of N_(SENS).
 5. Method according to claim 4, in which N_(SENS) is an even number and V_(ki)(t) is proportional to prod_(kj) ^(i) ₁ ^(i) ₂ ^(j) _(2p) ^((t)), where p=N_(SENS)/2.
 6. Method according to claim 5, in which N_(SENS)=2 and V_(kj)(t) is proportional to prod_(kj) ¹²(t).
 7. Method according to claim 4, in which N_(SENS)=3 and, for each reference point j, a resemblance function V_(kj)(t) is calculated, chosen from: ˜V_(kj)(t)=a3. [prod_(kj) ¹²(t)+prod_(kj) ²³(t)], and V_(kj)(t)=b3, {prod_(kj) ¹²(t)+prod_(kj) ²³(t)+prod_(kj) ¹³(t)}, a3 and b3 being constants.
 8. Method according to claim 7, in which a3=½ and b3=⅓.
 9. Method according to claim 4, in which N_(SENS)=4 and, for each reference point j, a resemblance function Vkj (t) is calculated chosen from: V _(kj)(t)=a4, prod _(kj) ¹²³⁴(t), V _(kj)(t)=b4, [prod _(kj) ¹²(t)+prod _(kj) ³⁴(t)], V _(kj)(t)=c4, [prod _(kj) ¹²(t)+prod _(kj) ²³(t)+prod _(kj) ³⁴(t)+prod _(kj) ¹⁴(t)], a4, b4 and c4 being constants.
 10. Method according to claim 9, in which a4=1, b4=½ and c4=¼.
 11. Method according to claim 4, in which the validation parameter is chosen from: $\mspace{79mu} {{{{MAXIMUM}\; 0j} = {{{Vkj}\left( {t = 0} \right)}}},\mspace{79mu} {{MAXIMUM}_{1j} = {{Max}\left( {{V_{kj}(t)}} \right)}},{{CONTRAST}_{1j} = \frac{{Max}\left( {MAXIMUM}_{0j} \right)}{\left( {{\Sigma_{j}{MAXIMUM}_{1j}} - {{Max}\left( {MAXIMUM}_{1j} \right)}} \right)/\left( {N_{REF} - 1} \right)}}}$      ENERGY = Σ_(i)(Σ_(t)[ski(t)]²).
 12. Method according to claim 11, in which only V_(kj)(t) is calculated from the time t=0 roughly corresponding to the impact, as being the real part of PROD_(kj) ^(i) ₁ ^(i) ₂ ^(j) _(2p) ^((ω)), and MAXIMUMoj is used as validation parameter.
 13. Method according to claim 11, in which at least one validation criterion is used, chosen from the following criteria: CONTRAST_(1j)>THRESHOLD₁, and THRESHOLD₁>1 CONTRAST_(1j)>THRESHOLD₁ and MAXIMUM_(0J)/MAXIMUMij^(NOISE)>THRESHOLD₂, where THRESHOLD₂>1 and MAXIMUMij^(NOISE) corresponds to the parameter MAXIMUM_(1J) of the signals processed previously and not having resulted in validation, MAXIMUMoj>THRESHOLD₃, with THRESHOLD₃>0 and MAXIMUMoj/MAXIMUMoj^(NOISE)>THRESHOLD₄ with THRESHOLD₄>1, where MAXIMUM_(0j) ^(NOISE) corresponds to the parameter MAXIMUMoj of the signals processed previously and not having resulted in validation, MAXIMUMoj/Average (MAXIMUMoj^(NOISE))>THRESHOLD₅, with THRESHOLD₅>1, ENERGY/ENERGY^(NOISE)>THRESHOLD₆, where THRESHOLD₆>1 and ENERGY^(NOISE) corresponds to the parameter ENERGY of the signals processed previously and not having resulted in validation.
 14. Method according to claim 1, in which the reference signals are predetermined theoretically.
 15. Method according to claim 14, in which the reference signals are used with said object without training phase.
 16. Method according to claim 1, wherein the reference signals are previously learned on a reference device identical to said object, then are used with said object without training phase.
 17. Device specially adapted to implement a method according to claim 1, this device comprising: an object provided with at least N_(SENS) acoustic sensors, N_(SENS) being a natural integer at least equal to 2, to pick up respectively N_(SENS) signals s_(ki)(t) from acoustic waves generated in the object by an impact on a surface belonging to said object, i being an index between 1 and N_(SENS) which denotes the corresponding sensor, means for calculating, for each reference point of index j, at least one validation parameter representative of at least one intercorrelation of complex phases representative of said picked up signals Ski(t) and of reference signals r_(ji)(t), each reference signal r_(ji)(t) corresponding to the signal that would be received by the sensor i in case of impact at a reference point j out of N_(REF) reference points belonging to said surface, N_(REF) being a natural integer at least equal to 1 and j being an index between 1 and N_(REF), and means for locating the impact by determining at least on reference point as close as possible to the point of impact, by applying at least one validation criterion to the validation parameter, characterized in said validation parameter is representative of at least one intercorrelation: PROD _(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((ω)) =øS _(ki1)(ω)φRji ₁(ω)*øSk _(i2)(ω)*øR _(ji2)(ω) . . . øS _(ki2p)(ω)*øRji _(2p)(ω) where: øS_(ki)(ω) is the complex phase of S_(ki)(ω), for i=i₁, i₂, . . . , i_(2p) øR_(ji)(ω) is the complex phase of R_(ji)(ω), for i=i₁, i₂, . . . i_(2p) * denotes the conjugate operator, applied to øS_(ki)(ω) for i=i_(2m) and to øR_(ji)(ω) for i=i_(2m-1), m is an integer between 1 and p; S_(ki)(ω) is the Fourier transform of S_(k)(t), R_(ji)(ω) is the Fourier transform of r_(ji)(t), p is a non-zero natural integer less than or equal to N_(SENS)/2, i₁, i₂, . . . i_(2p) are 2p indices denoting 2p sensors, each between 1 and N_(SENS).
 18. Method according to claim 3, in which, for each reference point, a resemblance function V_(kj)(t) is calculated, chosen from prod_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((t)), and a linear combination of a number of functions prod_(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((t)) corresponding to a number of subsets of 2p sensor out of N_(SENS).
 19. Method according to claim 12, in which at least one validation criterion is used, chosen from the following criteria: CONTRAST_(1j)>THRESHOLD₁, and THRESHOLD₁>1 CONTRAST_(1j)>THRESHOLD₁ and MAXIMUM_(0J)/MAXIMUMij^(NOISE)>THRESHOLD₂, where THRESHOLD₂>1 and MAXIMUMij^(NOISE) corresponds to the parameter MAXIMUM_(1J) of the signals processed previously and not having resulted in validation, MAXIMUMoj>THRESHOLD₃, with THRESHOLD₃>0 and MAXIMUMoj/MAXIMUMoj^(NOISE)>THRESHOLD₄ with THRESHOLD₄>1, where MAXIMUM_(0j) ^(NOISE) corresponds to the parameter MAXIMUMoj of the signals processed previously and not having resulted in validation, MAXIMUMoj/Average (MAXIMUMoj^(NOISE))>THRESHOLD₅, with THRESHOLD₅>1, ENERGY/ENERGY^(NOISE)>THRESHOLD₆, where THRESHOLD₆>1 and ENERGY^(NOISE) corresponds to the parameter ENERGY of the signals processed previously and not having resulted in validation.
 20. Device specially adapted to implement a method according to claim 2, this device comprising: an object provided with at least N_(SENS) acoustic sensors, N_(SENS) being a natural integer at least equal to 2, to pick up respectively N_(SENS) signals s_(ki)(t) from acoustic waves generated in the object by an impact on a surface belonging to said object, i being an index between 1 and N_(SENS) which denoted the corresponding sensor, means for calculation, for each reference point or index j, at least one validation parameter representative of at least one intercorrelation of complex phases representative of said picked up signals Ski(t) and of reference signals r_(ji)(t), each reference signal r_(ji)(t) corresponding to the signal that would be received by the sensor i in case of impact at a reference point j out of N_(REF) reference points belonging to said surface, N_(REF) being a natural integer at least equal to 1 and j being an index between 1 and N_(REF), and means for locating the impact by determining at least one reference point as close as possible to the point of impact, by applying at least one validation criterion to the validation parameter, characterized in that said validation parameter is representative of at least one intercorrelation: PROD _(kj) ^(i) ₁ ^(i) ₂ ^(i) _(2p) ^((ω)) =øS _(ki1)(ω)φRji ₁(ω)*øSk _(i2)(ω)*øR _(ji2)(ω) . . . øS _(ki2p)(ω)*øRji _(2p)(ω) where: øS_(ki)(ω) is the complex phase of S_(ki)(ω), for i=i₁, i₂, . . . , i_(2p) øR_(ji)(ω) is the complex phase of R_(ji)(ω), for i=i₁, i₂, . . . i_(2p) * denotes the conjugate operator, applied to øS_(ki)(ω) for i=i_(2m) and to øR_(ji)(ω) for i=i_(2m-1), m is an integer between 1 and p; S_(ki)(ω) is the Fourier transform of S_(k)(t), R_(ji)(ω) is the Fourier transform of r_(ji)(t), p is a non-zero natural integer less than or equal to N_(SENS)/2, i₁, i₂, . . . i_(2p) are 2p indices denoting 2p sensors, each between 1 and N_(SENS). 